Approaches to the Selection of Relevant Concepts in the Case of Noisy Data

Mikhail Klimushkin; Sergei Obiedkov; Camille Roth

doi:10.1007/978-3-642-11928-6_18

International Conference on Formal Concept Analysis

ICFCA 2010: Formal Concept Analysis pp 255-266

Approaches to the Selection of Relevant Concepts in the Case of Noisy Data

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Mikhail Klimushkin
Sergei Obiedkov
Camille Roth

Conference paper

DOI: 10.1007/978-3-642-11928-6_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5986)

Cite this paper as:: Klimushkin M., Obiedkov S., Roth C. (2010) Approaches to the Selection of Relevant Concepts in the Case of Noisy Data. In: Kwuida L., Sertkaya B. (eds) Formal Concept Analysis. ICFCA 2010. Lecture Notes in Computer Science, vol 5986. Springer, Berlin, Heidelberg

Abstract

Concept lattices built on noisy data tend to be large and hence hard to interpret. We introduce several measures that can be used in selecting relevant concepts and discuss how they can be combined together. We study their performance in a series of experiments.

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Authors and Affiliations

Mikhail Klimushkin
- 1
Sergei Obiedkov
- 1
Camille Roth
- 2

1.Higher School of EconomicsMoscowRussia
2.CAMS (CNRS/EHESS), Centre d’Analyse et de Mathématique Sociales, EHESSParisFrance

Approaches to the Selection of Relevant Concepts in the Case of Noisy Data

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